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https://github.com/amatofrancesco99/covid-19_new-cases-prediction

Covid-19 fourth wave it's close in Italy, but how we can know when the situation starts to be critical? We want to predict the new cases future trend, with a good model, knowing what happened in previous days.
https://github.com/amatofrancesco99/covid-19_new-cases-prediction

covid-19 covid-19-italy data-science jupyter-notebook lasso-regression linear-regression new-cases polynomial-regression python ridge-regression spline-regression

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Covid-19 fourth wave it's close in Italy, but how we can know when the situation starts to be critical? We want to predict the new cases future trend, with a good model, knowing what happened in previous days.

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README 

        
 Covid-19_New-cases-prediction



[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://github.com/Amatofrancesco99/Covid-19_New-positives-prediction/blob/main/LICENSE)

![](https://komarev.com/ghpvc/?username=Covid-19New-cases-prediction&label=Views&style=plastic&color=brightgreen)



![Jupyter Notebook](https://img.shields.io/badge/jupyter-%23FA0F00.svg?style=for-the-badge&logo=jupyter&logoColor=white)

![Python](https://img.shields.io/badge/python-3670A0?style=for-the-badge&logo=python&logoColor=ffdd54)





***





“Numbers have an important story to tell. They rely on you to give them a voice.”–Stephen Few


PRESENTATION



***



🇮🇹📈 Project description:



**[Covid-19](https://www.who.int/health-topics/coronavirus#tab=tab_1) fourth wave it's close** in Italy, but how we can know when the situation starts to be critical?




Since past events we know that the new cases growth is almost an exponential, when a wave starts, so we want to predict the future trend, with a **good model**, knowing what happened in previous days.




We also know that new cases, intensive care unit admissions and deaths are strictly correlated. When one increases, also the others are increasing in the same order of magnitde.




Building a good model is essential in order to **avoid** taking **bad decisions** (for example lockdown when not needed, or no restrictions when needed).




Obviously the prediction is really complicated, because a **lot of variables have to be take into account**.


An example: the third vaccination may produce a decrease effect in future trend (especially deaths).





As you may understood by the previous lines, the aim of this project is finding a good model to predict future trend, using **statistical learning theory**.




The regression functions that have been used are the following one:

 *  *[Linear Regression](https://en.wikipedia.org/wiki/Linear_regression)*

 *  *[Polynomial Regression](https://en.wikipedia.org/wiki/Polynomial_regression)*

 *  *[Lasso Regression](https://en.wikipedia.org/wiki/Lasso_(statistics))*

 *  *[Ridge Regression](https://www.mygreatlearning.com/blog/what-is-ridge-regression/)*

 *  *[Natural Cubic Splines](https://towardsdatascience.com/numerical-interpolation-natural-cubic-spline-52c1157b98ac)*

 

***



💾👨🏻‍💻 Data origin 



All data are public and provided by the [Dipartimento della Protezione Civile (DPC)](https://www.protezionecivile.gov.it/it/).




You can also find them at this [github link](https://github.com/pcm-dpc/COVID-19).



***



 🚀💻 Workplace configuration 



Before downloading the [source code](https://github.com/Amatofrancesco99/Covid-19_New-positives-prediction/blob/main/Covid-19%20Italy%20trend.ipynb), you should enable the dark theme on your jupyter notebook, in order to see graphs and other items in a proper way.




So you have to run the following lines of code in your terminal/command prompt:

```

pip install jupyterthemes

jt -t chesterish

```



Finally, you have to run the following code snippet, in order to get all the libraries that have been used in the project:

```

pip install DateTime

pip install ipywidgets

pip install python-math

pip install matplotlib

pip install numpy

pip install pandas

pip install patsy

pip install -U scikit-learn

```



***



📚🤔 Deepening:



Are you interested about this argument? 

 * Read this beautiful book: [The Elements of Statistical Learning](https://web.stanford.edu/~hastie/Papers/ESLII.pdf).

 * You can also see another project: [Gas consumption prediction](https://github.com/Amatofrancesco99/Gas_consumption-prediction)